
import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase  ):






				with open(a__  ) as metadata_file:
								A_													=	json.load(a__  )
				A_													=	LukeConfig(use_entity_aware_attention=a__			,    **metadata['''model_config''']  )

				# Load in the weights from the checkpoint_path
				A_													=	torch.load(a__			,    map_location='''cpu'''  )['module']

				# Load the entity vocab file
				A_													=	load_original_entity_vocab(a__  )
				# add an entry for [MASK2]
				A_													=	max(entity_vocab.values()  ) + 1
				config.entity_vocab_size += 1

				A_													=	XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']  )

				# Add special tokens to the token vocabulary for downstream tasks
				A_													=	AddedToken('''<ent>'''			,    lstrip=a__			,    rstrip=a__  )
				A_													=	AddedToken('''<ent2>'''			,    lstrip=a__			,    rstrip=a__  )
				tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}  )
				config.vocab_size += 2

				print(F"Saving tokenizer to {pytorch_dump_folder_path}"  )
				tokenizer.save_pretrained(a__  )
				with open(os.path.join(a__			,    '''tokenizer_config.json'''  )			,    '''r'''  ) as f:
								A_													=	json.load(a__  )
				A_													=	'MLukeTokenizer'
				with open(os.path.join(a__			,    '''tokenizer_config.json'''  )			,    '''w'''  ) as f:
								json.dump(a__			,    a__  )

				with open(os.path.join(a__			,    MLukeTokenizer.vocab_files_names['''entity_vocab_file''']  )			,    '''w'''  ) as f:
								json.dump(a__			,    a__  )

				A_													=	MLukeTokenizer.from_pretrained(a__  )

				# Initialize the embeddings of the special tokens
				A_													=	tokenizer.convert_tokens_to_ids(['''@''']  )[0]
				A_													=	tokenizer.convert_tokens_to_ids(['''#''']  )[0]

				A_													=	state_dict['embeddings.word_embeddings.weight']
				A_													=	word_emb[ent_init_index].unsqueeze(0  )
				A_													=	word_emb[enta_init_index].unsqueeze(0  )
				A_													=	torch.cat([word_emb, ent_emb, enta_emb]  )
				# add special tokens for 'entity_predictions.bias'
				for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
								A_													=	state_dict[bias_name]
								A_													=	decoder_bias[ent_init_index].unsqueeze(0  )
								A_													=	decoder_bias[enta_init_index].unsqueeze(0  )
								A_													=	torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]  )

				# Initialize the query layers of the entity-aware self-attention mechanism
				for layer_index in range(config.num_hidden_layers  ):
								for matrix_name in ["query.weight", "query.bias"]:
												A_													=	F"encoder.layer.{layer_index}.attention.self."
												A_													=	state_dict[prefix + matrix_name]
												A_													=	state_dict[prefix + matrix_name]
												A_													=	state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				A_													=	state_dict['entity_embeddings.entity_embeddings.weight']
				A_													=	entity_emb[entity_vocab['[MASK]']].unsqueeze(0  )
				A_													=	torch.cat([entity_emb, entity_mask_emb]  )
				# add [MASK2] for 'entity_predictions.bias'
				A_													=	state_dict['entity_predictions.bias']
				A_													=	entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0  )
				A_													=	torch.cat([entity_prediction_bias, entity_mask_bias]  )

				A_													=	LukeForMaskedLM(config=a__  ).eval()

				state_dict.pop('''entity_predictions.decoder.weight'''  )
				state_dict.pop('''lm_head.decoder.weight'''  )
				state_dict.pop('''lm_head.decoder.bias'''  )
				A_													=	OrderedDict()
				for key, value in state_dict.items():
								if not (key.startswith('''lm_head'''  ) or key.startswith('''entity_predictions'''  )):
												A_													=	state_dict[key]
								else:
												A_													=	state_dict[key]

				A_													=	model.load_state_dict(a__			,    strict=a__  )

				if set(a__  ) != {"luke.embeddings.position_ids"}:
								raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"  )
				if set(a__  ) != {
				    "lm_head.decoder.weight",
				    "lm_head.decoder.bias",
				    "entity_predictions.decoder.weight",
				}:
								raise ValueError(F"Unexpected missing_keys: {missing_keys}"  )

				model.tie_weights()
				assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
				assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

				# Check outputs
				A_													=	MLukeTokenizer.from_pretrained(a__			,    task='''entity_classification'''  )

				A_													=	'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
				A_													=	(0, 9)
				A_													=	tokenizer(a__			,    entity_spans=[span]			,    return_tensors='''pt'''  )

				A_													=	model(**a__  )

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								A_													=	torch.Size((1, 33, 7_68)  )
								A_													=	torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]  )

				if not (outputs.last_hidden_state.shape == expected_shape):
								raise ValueError(
								    F"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"  )
				if not torch.allclose(outputs.last_hidden_state[0, :3, :3]			,    a__			,    atol=1e-4  ):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								A_													=	torch.Size((1, 1, 7_68)  )
								A_													=	torch.tensor([[-0.1482, 0.0609, 0.0322]]  )

				if not (outputs.entity_last_hidden_state.shape == expected_shape):
								raise ValueError(
								    F"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
								    F" {expected_shape}"  )
				if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]			,    a__			,    atol=1e-4  ):
								raise ValueError

				# Verify masked word/entity prediction
				A_													=	MLukeTokenizer.from_pretrained(a__  )
				A_													=	'Tokyo is the capital of <mask>.'
				A_													=	(24, 30)
				A_													=	tokenizer(a__			,    entity_spans=[span]			,    return_tensors='''pt'''  )

				A_													=	model(**a__  )

				A_													=	encoding['input_ids'][0].tolist()
				A_													=	input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''  )  )
				A_													=	outputs.logits[0][mask_position_id].argmax(dim=-1  )
				assert "Japan" == tokenizer.decode(a__  )

				A_													=	outputs.entity_logits[0][0].argmax().item()
				A_													=	[
				    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
				]
				assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''  )][0] == "en:Japan"

				# Finally, save our PyTorch model and tokenizer
				print('''Saving PyTorch model to {}'''.format(a__  )  )
				model.save_pretrained(a__  )



def 							lowerCamelCase_	( __UpperCamelCase  ):
				A_													=	['[MASK]', '[PAD]', '[UNK]']

				A_													=	[json.loads(a__  ) for line in open(a__  )]

				A_													=	{}
				for entry in data:
								A_													=	entry['id']
								for entity_name, language in entry["entities"]:
												if entity_name in SPECIAL_TOKENS:
																A_													=	entity_id
																break
												A_													=	F"{language}:{entity_name}"
												A_													=	entity_id
				return new_mapping


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE :		int       			=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
						parser.add_argument(
						    "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
						)
						parser.add_argument(
						    "--entity_vocab_path",
						    default=None,
						    type=str,
						    help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
						)
						parser.add_argument(
						    "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
						)
						SCREAMING_SNAKE_CASE :		Dict       			=							parser.parse_args()
						convert_luke_checkpoint(
						    args.checkpoint_path,
						    args.metadata_path,
						    args.entity_vocab_path,
						    args.pytorch_dump_folder_path,
						    args.model_size,
						)







"""simple docstring"""


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				int
					lowercase_    :				TreeNode | None      =  None
					lowercase_    :				TreeNode | None      =  None


__SCREAMING_SNAKE_CASE   =   namedtuple('CoinsDistribResult', 'moves excess')


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if root is None:
												return 0

						# Validation
						def count_nodes(a__ ) -> int:
												if node is None:
																		return 0

												return count_nodes(node.left ) + count_nodes(node.right ) + 1

						def count_coins(a__ ) -> int:
												if node is None:
																		return 0

												return count_coins(node.left ) + count_coins(node.right ) + node.data

						if count_nodes(a__ ) != count_coins(a__ ):
												raise ValueError('The nodes number should be same as the number of coins' )

						# Main calculation
						def get_distrib(a__ ) -> CoinsDistribResult:

												if node is None:
																		return CoinsDistribResult(0     ,      1 )

												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			get_distrib(node.left )
												lowerCAmelCase	, lowerCAmelCase   :str						=			get_distrib(node.right )

												lowerCAmelCase   :str						=			1 - left_distrib_excess
												lowerCAmelCase   :Optional[Any]						=			1 - right_distrib_excess

												lowerCAmelCase   :Any						=			(
												    left_distrib_moves
												    + right_distrib_moves
												    + abs(a__ )
												    + abs(a__ )
												)
												lowerCAmelCase   :Tuple						=			node.data - coins_to_left - coins_to_right

												return CoinsDistribResult(a__     ,      a__ )

						return get_distrib(a__ )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()